从零基础入门Tensorflow2.0 ----二、4.4 wide & deep 模型（多输出）

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0. 前言

多输出问题。

1. 代码部分

1. 导入模块

import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl,np,pd,sklearn,tf,keras:
    print(module.__name__,module.__version__)

2. 读取数据

from sklearn.datasets import fetch_california_housing

# 房价预测
housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)

3. 划分样本

# 划分样本
from sklearn.model_selection import train_test_split

x_train_all,x_test,y_train_all,y_test = train_test_split(housing.data,housing.target,random_state=7)
x_train,x_valid,y_train,y_valid = train_test_split(x_train_all,y_train_all,random_state=11)

print(x_train.shape,y_train.shape)
print(x_valid.shape,y_valid.shape)
print(x_test.shape,y_test.shape)

4. 数据归一化

# 归一化
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)

5. 构建模型

注： 多输出问题在此

# 多输出（函数式方法）
input_wide = keras.layers.Input(shape=[5])
input_deep = keras.layers.Input(shape=[6])
hidden1 = keras.layers.Dense(30,activation='relu')(input_deep)
hidden2 = keras.layers.Dense(30,activation='relu')(hidden1)
concat = keras.layers.concatenate([input_wide,hidden2])
output = keras.layers.Dense(1)(concat)
output2 = keras.layers.Dense(1)(hidden2)
model = keras.models.Model(inputs=[input_wide,input_deep],outputs=[output,output2])

# 打印model信息
model.summary()
# 编译
model.compile(loss='mean_squared_error',optimizer="adam")
# 回调函数
callbacks = [keras.callbacks.EarlyStopping(patience=5,min_delta=1e-2)]

6. 训练

# 一共8个特征，wide前5个，deep后6个
x_train_scaled_wide = x_train_scaled[:,:5]
x_train_scaled_deep = x_train_scaled[:,2:]
x_valid_scaled_wide = x_valid_scaled[:,:5]
x_valid_scaled_deep = x_valid_scaled[:,2:]
x_test_scaled_wide = x_test_scaled[:,:5]
x_test_scaled_deep = x_test_scaled[:,2:]


#训练
history = model.fit([x_train_scaled_wide,x_train_scaled_deep],[y_train,y_train],validation_data=([x_valid_scaled_wide,x_valid_scaled_deep],[y_valid,y_valid]),epochs=100,callbacks=callbacks)

7. 学习曲线

# 学习曲线
def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8,5))
    plt.grid(True)
    plt.gca().set_ylim(0,1)
    plt.show()
plot_learning_curves(history)

8. 测试集上

model.evaluate([x_test_scaled_wide,x_test_scaled_deep],[y_test,y_test])